Applied AI Scientist - Quantum AI

Kadence is partnered with a Quantum AI company that is building quantum-accelerated AI infrastructure, combining multiple qubit types in a single fault-tolerant architecture to tackle the cost, scale, and speed bottlenecks facing modern AI. We are hiring an Applied AI Scientist, Technology Partnerships.Location: San Francisco (On-site), occasional travel for partner meetingsTheir mission is to accelerate the path to advanced AI by merging quantum computing with machine learning, and we're looking for people who want to help define that intersection.The RoleTheir algorithms team is developing quantum approaches to training, inference, and reasoning, including quantum-native generative modeling, largely prototyped today on classical hardware so results transfer directly once quantum processors come online.This work increasingly meets real-world problems through partner collaborations. We need someone to carry their methods into each partner's domain and rigorously test how well they hold up. You'll:Assess where our quantum-native generative methods genuinely apply, which algorithm, under what assumptions, in what regimeBuild and run proof-of-concept work, adapting research implementations to each partner's domainValidate results against whatever the partner already trusts- molecular dynamics, Monte Carlo, a classical solver, or their production modelPresent findings in technical working sessions and feed learnings back into our research roadmapDesign rigorous benchmarks (quantum, classical, hybrid) against the strongest available baseline, not a convenient oneUse approximate simulation, circuit emulation, and analytic resource models as fits the questionProduce the charts, comparisons, and reproducible artifacts behind published and partner-facing resultsYou're a Good Fit If YouShip code daily, and your results are reproducible by othersAre quantitatively sharp enough to judge whether a result is right, not just whether the pipeline ranCan hold your own in a technical conversation with domain experts outside your training and find the real bottleneckAre rigorous about where quantum methods help - and where they don'tCan juggle multiple engagements in parallel without letting any go staleStrong Candidates May HaveMS/PhD in a computational field (physics, chemistry, applied math, computational biology, engineering) or equivalent industry depthStrong Python and scientific computing skills: linear algebra, ODE/SDE solvers, Monte Carlo methods, uncertainty quantificationGenerative modeling experience: diffusion, flow matching, normalizing flows, score-based or energy-based modelsExposure to tensor networks or other structured representations for high-dimensional problemsApplied experience in molecular/materials modeling, time-series forecasting, physical simulation, or generative designStrong technical writing and a track record of reproducible research or collaborative publicationsCuriosity about quantum computing (prior experience welcome, not required)Why It MattersOur hardware is a multi-year build - the algorithms need to be ready for real workloads the day it arrives. Technical partnerships are how we stress-test which workloads matter and prove out the methods on them.Culture & BenefitsVisa sponsorship considered whenever possibleCompetitive salary and meaningful equityCompany-sponsored health coverageRegular team offsites and social eventsUnlimited PTOCompensation Offering is dependant on experience, range: $200k-$270k base, plus equity.